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README.md

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+---
+base_model: minishlab/potion-multilingual-128m
+language:
+- en
+- multilingual
+- af
+- am
+- ar
+- az
+- be
+- bg
+- bn
+- ca
+- ceb
+- co
+- cs
+- cy
+- da
+- de
+- el
+- eo
+- es
+- et
+- eu
+- fa
+- fi
+- fil
+- fr
+- fy
+- ga
+- gd
+- gl
+- gu
+- ha
+- haw
+- hi
+- hmn
+- ht
+- hu
+- hy
+- id
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+- jv
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+- km
+- kn
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+- ku
+- ky
+- la
+- lb
+- lo
+- lt
+- lv
+- mg
+- mi
+- mk
+- ml
+- mn
+- mr
+- ms
+- mt
+- my
+- ne
+- nl
+- 'no'
+- ny
+- pa
+- pl
+- ps
+- pt
+- ro
+- ru
+- sd
+- si
+- sk
+- sl
+- sm
+- sn
+- so
+- sq
+- sr
+- st
+- su
+- sv
+- sw
+- ta
+- te
+- tg
+- th
+- tr
+- uk
+- ur
+- uz
+- vi
+- xh
+- yi
+- yo
+- zh
+- zu
+library_name: model2vec
+license: mit
+model_name: potion-multilingual-128m-onnx
+tags:
+- embeddings
+- static-embeddings
+- sentence-transformers
+- onnx
+---
+
+# potion-multilingual-128m-onnx Model Card
+
+This is an ONNX export of the [minishlab/potion-multilingual-128m](https://huggingface.co/minishlab/potion-multilingual-128m) [Model2Vec](https://github.com/MinishLab/model2vec) model, produced with the [ONNX](https://onnx.ai/) runtime. [Model2Vec](https://github.com/MinishLab/model2vec) models use static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. This ONNX export lets you run the model with `onnxruntime` or `transformers.js`, without depending on the `model2vec` package.
+
+## Usage
+
+### Using ONNX Runtime
+
+```python
+import onnxruntime as ort
+from transformers import AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("potion-multilingual-128m-onnx")
+session = ort.InferenceSession("model.onnx")
+
+encodings = tokenizer(["Example sentence"], padding=True, return_tensors="np")
+embeddings = session.run(None, dict(encodings))[0]
+```
+
+### Using the original Model2Vec model
+
+If you don't need the ONNX runtime, you can load the original model with the [Model2Vec library](https://github.com/MinishLab/model2vec) instead:
+```python
+from model2vec import StaticModel
+
+model = StaticModel.from_pretrained("minishlab/potion-multilingual-128m")
+embeddings = model.encode(["Example sentence"])
+```
+
+## Additional Resources
+
+- [Model2Vec Repo](https://github.com/MinishLab/model2vec)
+- [Model2Vec Base Models](https://huggingface.co/collections/minishlab/model2vec-base-models-66fd9dd9b7c3b3c0f25ca90e)
+- [Model2Vec Results](https://github.com/MinishLab/model2vec/tree/main/results)
+- [Model2Vec Docs](https://minish.ai/packages/model2vec/introduction)
+
+## Library Authors
+
+Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled).
+
+## Citation
+
+Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work.
+```
+@software{minishlab2024model2vec,
+  author       = {Stephan Tulkens and {van Dongen}, Thomas},
+  title        = {Model2Vec: Fast State-of-the-Art Static Embeddings},
+  year         = {2024},
+  publisher    = {Zenodo},
+  doi          = {10.5281/zenodo.17270888},
+  url          = {https://github.com/MinishLab/model2vec},
+  license      = {MIT}
+}
+```

config.json

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+{
+  "pad_token_id": 0
+}

model.onnx

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special_tokens_map.json

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+  "pad_token": "[PAD]"
+}

tokenizer.json

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tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "clean_up_tokenization_spaces": false,
+  "model_max_length": 32768,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": "[UNK]"
+}